Formally, we denote the global token as the first row vector of \({X}_e\) , \(\emph {i.e.}, {x}_1\) . Thus \({q}_1\) , \({k}_1\)  and \({v}_1\)  are the query, key and value projected from the global token, respectively. To explicitly model the connection between global and local visual features, we only calculate the correlation between each local token and the global token, i.e., \(Correlation({x}_i, {x}_1)\) , as well as its self-correlation, i.e., \(Correlation({x}_i, {x}_i)\) . Then correlation scores are normalized by softmax to further re-weight the values.
We exploit a suppression matrix [1]} \({S}^{(N+1)\times (N+1)}\)  to suppress the correlation of other tokens, where
\({S}^{(N+1)\times (N+1)} = [s_{ij}],\quad s_{ij}=\left\lbrace \begin{array}{ll}0, &\mathrm {if}\; i=j \; \mathrm {or} \; j=1 \\\lambda , &\mathrm {otherwise}.\end{array}\right.\) 
